Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations2,312,944
Missing cells1,305,751
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 GiB
Average record size in memory1011.9 B

Variable types

Text8
Categorical4
Numeric5
Boolean4
DateTime3

Alerts

Installs is highly overall correlated with Maximum Installs and 1 other fieldsHigh correlation
Maximum Installs is highly overall correlated with Installs and 3 other fieldsHigh correlation
Minimum Installs is highly overall correlated with Installs and 3 other fieldsHigh correlation
Rating is highly overall correlated with Maximum Installs and 2 other fieldsHigh correlation
Rating Count is highly overall correlated with Maximum Installs and 2 other fieldsHigh correlation
Free is highly imbalanced (86.1%) Imbalance
Currency is highly imbalanced (99.8%) Imbalance
Content Rating is highly imbalanced (72.9%) Imbalance
In App Purchases is highly imbalanced (58.2%) Imbalance
Editors Choice is highly imbalanced (99.5%) Imbalance
Developer Website has 760835 (32.9%) missing values Missing
Released has 71053 (3.1%) missing values Missing
Privacy Policy has 420953 (18.2%) missing values Missing
Rating Count is highly skewed (γ1 = 425.827796) Skewed
Minimum Installs is highly skewed (γ1 = 351.1240817) Skewed
Maximum Installs is highly skewed (γ1 = 302.0158966) Skewed
Price is highly skewed (γ1 = 98.89108745) Skewed
App Id has unique values Unique
Rating has 1059762 (45.8%) zeros Zeros
Rating Count has 1059762 (45.8%) zeros Zeros
Price has 2268011 (98.1%) zeros Zeros

Reproduction

Analysis started2025-04-03 17:54:54.307724
Analysis finished2025-04-03 18:19:05.156517
Duration24 minutes and 10.85 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct2177943
Distinct (%)94.2%
Missing5
Missing (%)< 0.1%
Memory size179.6 MiB
2025-04-03T20:19:05.853729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length36
Mean length21.918764
Min length1

Characters and Unicode

Total characters50,696,765
Distinct characters9,901
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2,110,458 ?
Unique (%)91.2%

Sample

1st rowGakondo
2nd rowAmpere Battery Info
3rd rowVibook
4th rowSmart City Trichy Public Service Vehicles 17UCS548
5th rowGROW.me
ValueCountFrequency (%)
411659
 
5.0%
for 65891
 
0.8%
app 63372
 
0.8%
radio 62789
 
0.8%
the 54770
 
0.7%
free 52603
 
0.6%
and 48523
 
0.6%
game 40134
 
0.5%
wallpaper 36017
 
0.4%
of 33626
 
0.4%
Other values (885415) 7367377
89.4%
2025-04-03T20:19:06.550322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5939158
 
11.7%
e 3693779
 
7.3%
a 3476838
 
6.9%
i 2692836
 
5.3%
o 2581981
 
5.1%
r 2528042
 
5.0%
n 2208482
 
4.4%
t 1997829
 
3.9%
s 1790073
 
3.5%
l 1778158
 
3.5%
Other values (9891) 22009589
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50696765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5939158
 
11.7%
e 3693779
 
7.3%
a 3476838
 
6.9%
i 2692836
 
5.3%
o 2581981
 
5.1%
r 2528042
 
5.0%
n 2208482
 
4.4%
t 1997829
 
3.9%
s 1790073
 
3.5%
l 1778158
 
3.5%
Other values (9891) 22009589
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50696765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5939158
 
11.7%
e 3693779
 
7.3%
a 3476838
 
6.9%
i 2692836
 
5.3%
o 2581981
 
5.1%
r 2528042
 
5.0%
n 2208482
 
4.4%
t 1997829
 
3.9%
s 1790073
 
3.5%
l 1778158
 
3.5%
Other values (9891) 22009589
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50696765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5939158
 
11.7%
e 3693779
 
7.3%
a 3476838
 
6.9%
i 2692836
 
5.3%
o 2581981
 
5.1%
r 2528042
 
5.0%
n 2208482
 
4.4%
t 1997829
 
3.9%
s 1790073
 
3.5%
l 1778158
 
3.5%
Other values (9891) 22009589
43.4%

App Id
Text

Unique 

Distinct2312944
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size168.0 MiB
2025-04-03T20:19:07.437076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length150
Median length139
Mean length27.15986
Min length3

Characters and Unicode

Total characters62,819,235
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2,312,944 ?
Unique (%)100.0%

Sample

1st rowcom.ishakwe.gakondo
2nd rowcom.webserveis.batteryinfo
3rd rowcom.doantiepvien.crm
4th rowcst.stJoseph.ug17ucs548
5th rowcom.horodyski.grower
ValueCountFrequency (%)
biblia.em.audio 6
 
< 0.1%
mil.army 6
 
< 0.1%
baixar.biblia.sagrada.gratis 5
 
< 0.1%
com.yy.musicfm.global 3
 
< 0.1%
catholic.bible 3
 
< 0.1%
study.bible 3
 
< 0.1%
com.iloveyoulivewallpaper 3
 
< 0.1%
bible.kjv 3
 
< 0.1%
com.kkkeyboard.emoji.keyboard.theme.spacecraft 2
 
< 0.1%
com.desibit.animewatch 2
 
< 0.1%
Other values (2312535) 2312908
> 99.9%
2025-04-03T20:19:08.414408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 5398156
 
8.6%
. 5384346
 
8.6%
a 5272398
 
8.4%
e 4674261
 
7.4%
c 3655165
 
5.8%
i 3636745
 
5.8%
m 3530987
 
5.6%
r 3408963
 
5.4%
s 3056831
 
4.9%
t 2911132
 
4.6%
Other values (54) 21890251
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62819235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5398156
 
8.6%
. 5384346
 
8.6%
a 5272398
 
8.4%
e 4674261
 
7.4%
c 3655165
 
5.8%
i 3636745
 
5.8%
m 3530987
 
5.6%
r 3408963
 
5.4%
s 3056831
 
4.9%
t 2911132
 
4.6%
Other values (54) 21890251
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62819235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5398156
 
8.6%
. 5384346
 
8.6%
a 5272398
 
8.4%
e 4674261
 
7.4%
c 3655165
 
5.8%
i 3636745
 
5.8%
m 3530987
 
5.6%
r 3408963
 
5.4%
s 3056831
 
4.9%
t 2911132
 
4.6%
Other values (54) 21890251
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62819235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5398156
 
8.6%
. 5384346
 
8.6%
a 5272398
 
8.4%
e 4674261
 
7.4%
c 3655165
 
5.8%
i 3636745
 
5.8%
m 3530987
 
5.6%
r 3408963
 
5.4%
s 3056831
 
4.9%
t 2911132
 
4.6%
Other values (54) 21890251
34.8%

Category
Categorical

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size131.0 MiB
Education
241090 
Music & Audio
154906 
Tools
 
143988
Business
 
143771
Entertainment
 
138276
Other values (43)
1490913 

Length

Max length23
Median length15
Mean length10.371109
Min length4

Characters and Unicode

Total characters23,987,795
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdventure
2nd rowTools
3rd rowProductivity
4th rowCommunication
5th rowTools

Common Values

ValueCountFrequency (%)
Education 241090
 
10.4%
Music & Audio 154906
 
6.7%
Tools 143988
 
6.2%
Business 143771
 
6.2%
Entertainment 138276
 
6.0%
Lifestyle 118331
 
5.1%
Books & Reference 116728
 
5.0%
Personalization 89210
 
3.9%
Health & Fitness 83510
 
3.6%
Productivity 79698
 
3.4%
Other values (38) 1003436
43.4%

Length

2025-04-03T20:19:08.476461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
636289
 
17.6%
education 241090
 
6.7%
music 159108
 
4.4%
audio 154906
 
4.3%
tools 143988
 
4.0%
business 143771
 
4.0%
entertainment 138276
 
3.8%
lifestyle 118331
 
3.3%
books 116728
 
3.2%
reference 116728
 
3.2%
Other values (52) 1640356
45.4%

Most occurring characters

ValueCountFrequency (%)
i 2093771
 
8.7%
e 1965664
 
8.2%
o 1941902
 
8.1%
n 1790669
 
7.5%
t 1553226
 
6.5%
s 1541525
 
6.4%
a 1502077
 
6.3%
1296627
 
5.4%
u 1040935
 
4.3%
c 988503
 
4.1%
Other values (31) 8272896
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23987795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2093771
 
8.7%
e 1965664
 
8.2%
o 1941902
 
8.1%
n 1790669
 
7.5%
t 1553226
 
6.5%
s 1541525
 
6.4%
a 1502077
 
6.3%
1296627
 
5.4%
u 1040935
 
4.3%
c 988503
 
4.1%
Other values (31) 8272896
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23987795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2093771
 
8.7%
e 1965664
 
8.2%
o 1941902
 
8.1%
n 1790669
 
7.5%
t 1553226
 
6.5%
s 1541525
 
6.4%
a 1502077
 
6.3%
1296627
 
5.4%
u 1040935
 
4.3%
c 988503
 
4.1%
Other values (31) 8272896
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23987795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2093771
 
8.7%
e 1965664
 
8.2%
o 1941902
 
8.1%
n 1790669
 
7.5%
t 1553226
 
6.5%
s 1541525
 
6.4%
a 1502077
 
6.3%
1296627
 
5.4%
u 1040935
 
4.3%
c 988503
 
4.1%
Other values (31) 8272896
34.5%

Rating
Real number (ℝ)

High correlation  Zeros 

Distinct42
Distinct (%)< 0.1%
Missing22883
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.2031515
Minimum0
Maximum5
Zeros1059762
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size17.6 MiB
2025-04-03T20:19:08.539514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.9
Q34.3
95-th percentile4.9
Maximum5
Range5
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation2.1062225
Coefficient of variation (CV)0.95600438
Kurtosis-1.8598136
Mean2.2031515
Median Absolute Deviation (MAD)2.1
Skewness-0.0020644962
Sum5045351.4
Variance4.4361733
MonotonicityNot monotonic
2025-04-03T20:19:08.603570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 1059762
45.8%
5 100122
 
4.3%
4.2 87993
 
3.8%
4.4 86304
 
3.7%
4.3 83276
 
3.6%
4.6 78302
 
3.4%
4.5 76753
 
3.3%
4.1 69723
 
3.0%
4 67342
 
2.9%
4.7 62205
 
2.7%
Other values (32) 518279
22.4%
ValueCountFrequency (%)
0 1059762
45.8%
1 713
 
< 0.1%
1.1 236
 
< 0.1%
1.2 531
 
< 0.1%
1.3 579
 
< 0.1%
1.4 1011
 
< 0.1%
1.5 1157
 
0.1%
1.6 1644
 
0.1%
1.7 1928
 
0.1%
1.8 2957
 
0.1%
ValueCountFrequency (%)
5 100122
4.3%
4.9 44524
1.9%
4.8 61109
2.6%
4.7 62205
2.7%
4.6 78302
3.4%
4.5 76753
3.3%
4.4 86304
3.7%
4.3 83276
3.6%
4.2 87993
3.8%
4.1 69723
3.0%

Rating Count
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct38482
Distinct (%)1.7%
Missing22883
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2864.8389
Minimum0
Maximum1.3855757 × 108
Zeros1059762
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size17.6 MiB
2025-04-03T20:19:08.667123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q342
95-th percentile1397
Maximum1.3855757 × 108
Range1.3855757 × 108
Interquartile range (IQR)42

Descriptive statistics

Standard deviation212162.57
Coefficient of variation (CV)74.057418
Kurtosis227234.89
Mean2864.8389
Median Absolute Deviation (MAD)6
Skewness425.8278
Sum6.5606558 × 109
Variance4.5012957 × 1010
MonotonicityNot monotonic
2025-04-03T20:19:08.735681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1059762
45.8%
5 64288
 
2.8%
6 54325
 
2.3%
7 46838
 
2.0%
8 40585
 
1.8%
9 35953
 
1.6%
10 32294
 
1.4%
11 29142
 
1.3%
12 26134
 
1.1%
13 23629
 
1.0%
Other values (38472) 877111
37.9%
(Missing) 22883
 
1.0%
ValueCountFrequency (%)
0 1059762
45.8%
5 64288
 
2.8%
6 54325
 
2.3%
7 46838
 
2.0%
8 40585
 
1.8%
9 35953
 
1.6%
10 32294
 
1.4%
11 29142
 
1.3%
12 26134
 
1.1%
13 23629
 
1.0%
ValueCountFrequency (%)
138557570 1
< 0.1%
120206190 1
< 0.1%
117850066 1
< 0.1%
112440547 1
< 0.1%
89177097 1
< 0.1%
78563229 1
< 0.1%
56025424 1
< 0.1%
37479011 1
< 0.1%
36446381 1
< 0.1%
35369236 1
< 0.1%

Installs
Categorical

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing107
Missing (%)< 0.1%
Memory size119.0 MiB
100+
443368 
1,000+
398199 
10+
300156 
10,000+
256723 
500+
189077 
Other values (17)
725314 

Length

Max length15
Median length14
Mean length4.9309407
Min length2

Characters and Unicode

Total characters11,404,462
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10+
2nd row5,000+
3rd row50+
4th row10+
5th row100+

Common Values

ValueCountFrequency (%)
100+ 443368
19.2%
1,000+ 398199
17.2%
10+ 300156
13.0%
10,000+ 256723
11.1%
500+ 189077
8.2%
50+ 170465
 
7.4%
5,000+ 143593
 
6.2%
100,000+ 110257
 
4.8%
50,000+ 75359
 
3.3%
5+ 73772
 
3.2%
Other values (12) 151868
 
6.6%

Length

2025-04-03T20:19:08.803739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100 443368
19.2%
1,000 398199
17.2%
10 300156
13.0%
10,000 256723
11.1%
500 189077
8.2%
50 170465
 
7.4%
5,000 143593
 
6.2%
100,000 110257
 
4.8%
50,000 75359
 
3.3%
5 73772
 
3.2%
Other values (12) 151868
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 5683251
49.8%
+ 2312837
20.3%
1 1614495
 
14.2%
, 1107103
 
9.7%
5 686776
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11404462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5683251
49.8%
+ 2312837
20.3%
1 1614495
 
14.2%
, 1107103
 
9.7%
5 686776
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11404462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5683251
49.8%
+ 2312837
20.3%
1 1614495
 
14.2%
, 1107103
 
9.7%
5 686776
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11404462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5683251
49.8%
+ 2312837
20.3%
1 1614495
 
14.2%
, 1107103
 
9.7%
5 686776
 
6.0%

Minimum Installs
Real number (ℝ)

High correlation  Skewed 

Distinct22
Distinct (%)< 0.1%
Missing107
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean183445.21
Minimum0
Maximum1 × 1010
Zeros11566
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 MiB
2025-04-03T20:19:08.856284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q150
median500
Q35000
95-th percentile100000
Maximum1 × 1010
Range1 × 1010
Interquartile range (IQR)4950

Descriptive statistics

Standard deviation15131439
Coefficient of variation (CV)82.484785
Kurtosis155112.87
Mean183445.21
Median Absolute Deviation (MAD)495
Skewness351.12408
Sum4.2427888 × 1011
Variance2.2896045 × 1014
MonotonicityNot monotonic
2025-04-03T20:19:08.908328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
100 443368
19.2%
1000 398199
17.2%
10 300156
13.0%
10000 256723
11.1%
500 189077
8.2%
50 170465
 
7.4%
5000 143593
 
6.2%
100000 110257
 
4.8%
50000 75359
 
3.3%
5 73772
 
3.2%
Other values (12) 151868
 
6.6%
ValueCountFrequency (%)
0 11566
 
0.5%
1 65345
 
2.8%
5 73772
 
3.2%
10 300156
13.0%
50 170465
 
7.4%
100 443368
19.2%
500 189077
8.2%
1000 398199
17.2%
5000 143593
 
6.2%
10000 256723
11.1%
ValueCountFrequency (%)
1 × 10101
 
< 0.1%
5000000000 14
 
< 0.1%
1000000000 55
 
< 0.1%
500000000 65
 
< 0.1%
100000000 549
 
< 0.1%
50000000 824
 
< 0.1%
10000000 6192
 
0.3%
5000000 6595
 
0.3%
1000000 33650
1.5%
500000 27012
1.2%

Maximum Installs
Real number (ℝ)

High correlation  Skewed 

Distinct251563
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean320201.71
Minimum0
Maximum1.2057627 × 1010
Zeros11589
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 MiB
2025-04-03T20:19:08.971381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q184
median695
Q37354
95-th percentile238178.75
Maximum1.2057627 × 1010
Range1.2057627 × 1010
Interquartile range (IQR)7270

Descriptive statistics

Standard deviation23554955
Coefficient of variation (CV)73.562863
Kurtosis110642.74
Mean320201.71
Median Absolute Deviation (MAD)683
Skewness302.0159
Sum7.4060863 × 1011
Variance5.548359 × 1014
MonotonicityNot monotonic
2025-04-03T20:19:09.041942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 16815
 
0.7%
2 16623
 
0.7%
4 16319
 
0.7%
6 15842
 
0.7%
5 15695
 
0.7%
1 15605
 
0.7%
7 14942
 
0.6%
8 14142
 
0.6%
10 13605
 
0.6%
9 13154
 
0.6%
Other values (251553) 2160202
93.4%
ValueCountFrequency (%)
0 11589
0.5%
1 15605
0.7%
2 16623
0.7%
3 16815
0.7%
4 16319
0.7%
5 15695
0.7%
6 15842
0.7%
7 14942
0.6%
8 14142
0.6%
9 13154
0.6%
ValueCountFrequency (%)
1.205762702 × 10101
< 0.1%
9766230924 1
< 0.1%
9154248491 1
< 0.1%
9141671889 1
< 0.1%
9034404884 1
< 0.1%
8925640788 1
< 0.1%
8756574289 1
< 0.1%
7408134567 1
< 0.1%
7028265259 1
< 0.1%
6782619635 1
< 0.1%

Free
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
True
2267876 
False
 
45068
ValueCountFrequency (%)
True 2267876
98.1%
False 45068
 
1.9%
2025-04-03T20:19:09.089483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Price
Real number (ℝ)

Skewed  Zeros 

Distinct1063
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10349916
Minimum0
Maximum400
Zeros2268011
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size17.6 MiB
2025-04-03T20:19:09.137023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum400
Range400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6331266
Coefficient of variation (CV)25.441043
Kurtosis12526.663
Mean0.10349916
Median Absolute Deviation (MAD)0
Skewness98.891087
Sum239387.76
Variance6.9333555
MonotonicityNot monotonic
2025-04-03T20:19:09.209084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2268011
98.1%
0.99 11851
 
0.5%
1.99 5817
 
0.3%
2.99 3921
 
0.2%
1.49 3823
 
0.2%
4.99 2496
 
0.1%
3.99 2404
 
0.1%
2.49 2182
 
0.1%
3.49 1272
 
0.1%
9.99 878
 
< 0.1%
Other values (1053) 10289
 
0.4%
ValueCountFrequency (%)
0 2268011
98.1%
0.194824 2
 
< 0.1%
0.204735 1
 
< 0.1%
0.207889 8
 
< 0.1%
0.21122 1
 
< 0.1%
0.263326 1
 
< 0.1%
0.273542 1
 
< 0.1%
0.393585 1
 
< 0.1%
0.415779 2
 
< 0.1%
0.449011 1
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
399.99 23
< 0.1%
394.99 2
 
< 0.1%
389.99 3
 
< 0.1%
384.99 1
 
< 0.1%
379.99 5
 
< 0.1%
374.99 1
 
< 0.1%
369.99 1
 
< 0.1%
365.99 1
 
< 0.1%
364.99 1
 
< 0.1%

Currency
Categorical

Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing135
Missing (%)< 0.1%
Memory size114.7 MiB
USD
2311548 
XXX
 
1236
EUR
 
6
INR
 
5
GBP
 
3
Other values (10)
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6,938,427
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 2311548
99.9%
XXX 1236
 
0.1%
EUR 6
 
< 0.1%
INR 5
 
< 0.1%
GBP 3
 
< 0.1%
CAD 2
 
< 0.1%
VND 1
 
< 0.1%
BRL 1
 
< 0.1%
KRW 1
 
< 0.1%
TRY 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 135
 
< 0.1%

Length

2025-04-03T20:19:09.269636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 2311548
99.9%
xxx 1236
 
0.1%
eur 6
 
< 0.1%
inr 5
 
< 0.1%
gbp 3
 
< 0.1%
cad 2
 
< 0.1%
vnd 1
 
< 0.1%
brl 1
 
< 0.1%
krw 1
 
< 0.1%
try 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U 2311556
33.3%
D 2311553
33.3%
S 2311549
33.3%
X 3708
 
0.1%
R 17
 
< 0.1%
E 6
 
< 0.1%
N 6
 
< 0.1%
I 5
 
< 0.1%
B 5
 
< 0.1%
G 4
 
< 0.1%
Other values (10) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6938427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 2311556
33.3%
D 2311553
33.3%
S 2311549
33.3%
X 3708
 
0.1%
R 17
 
< 0.1%
E 6
 
< 0.1%
N 6
 
< 0.1%
I 5
 
< 0.1%
B 5
 
< 0.1%
G 4
 
< 0.1%
Other values (10) 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6938427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 2311556
33.3%
D 2311553
33.3%
S 2311549
33.3%
X 3708
 
0.1%
R 17
 
< 0.1%
E 6
 
< 0.1%
N 6
 
< 0.1%
I 5
 
< 0.1%
B 5
 
< 0.1%
G 4
 
< 0.1%
Other values (10) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6938427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 2311556
33.3%
D 2311553
33.3%
S 2311549
33.3%
X 3708
 
0.1%
R 17
 
< 0.1%
E 6
 
< 0.1%
N 6
 
< 0.1%
I 5
 
< 0.1%
B 5
 
< 0.1%
G 4
 
< 0.1%
Other values (10) 18
 
< 0.1%

Size
Text

Distinct1657
Distinct (%)0.1%
Missing196
Missing (%)< 0.1%
Memory size116.8 MiB
2025-04-03T20:19:09.437779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length6
Mean length3.9755585
Min length3

Characters and Unicode

Total characters9,194,465
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)< 0.1%

Sample

1st row10M
2nd row2.9M
3rd row3.7M
4th row1.8M
5th row6.2M
ValueCountFrequency (%)
varies 74777
 
3.0%
with 74777
 
3.0%
device 74777
 
3.0%
11m 62157
 
2.5%
12m 56080
 
2.3%
13m 48034
 
2.0%
14m 45211
 
1.8%
16m 42474
 
1.7%
15m 41306
 
1.7%
17m 37244
 
1.5%
Other values (1649) 1905465
77.4%
2025-04-03T20:19:09.662471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 2201705
23.9%
. 1067961
11.6%
1 805540
 
8.8%
2 637993
 
6.9%
3 566660
 
6.2%
4 482479
 
5.2%
5 433276
 
4.7%
6 378006
 
4.1%
7 355064
 
3.9%
8 332712
 
3.6%
Other values (18) 1933069
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9194465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 2201705
23.9%
. 1067961
11.6%
1 805540
 
8.8%
2 637993
 
6.9%
3 566660
 
6.2%
4 482479
 
5.2%
5 433276
 
4.7%
6 378006
 
4.1%
7 355064
 
3.9%
8 332712
 
3.6%
Other values (18) 1933069
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9194465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 2201705
23.9%
. 1067961
11.6%
1 805540
 
8.8%
2 637993
 
6.9%
3 566660
 
6.2%
4 482479
 
5.2%
5 433276
 
4.7%
6 378006
 
4.1%
7 355064
 
3.9%
8 332712
 
3.6%
Other values (18) 1933069
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9194465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 2201705
23.9%
. 1067961
11.6%
1 805540
 
8.8%
2 637993
 
6.9%
3 566660
 
6.2%
4 482479
 
5.2%
5 433276
 
4.7%
6 378006
 
4.1%
7 355064
 
3.9%
8 332712
 
3.6%
Other values (18) 1933069
21.0%
Distinct154
Distinct (%)< 0.1%
Missing6530
Missing (%)0.3%
Memory size130.7 MiB
2025-04-03T20:19:09.729527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length10
Mean length10.341213
Min length3

Characters and Unicode

Total characters23,851,119
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)< 0.1%

Sample

1st row7.1 and up
2nd row5.0 and up
3rd row4.0.3 and up
4th row4.0.3 and up
5th row4.1 and up
ValueCountFrequency (%)
and 2259734
32.7%
up 2259734
32.7%
4.1 604534
 
8.7%
5.0 397035
 
5.7%
4.4 390396
 
5.6%
4.0.3 180524
 
2.6%
4.0 153527
 
2.2%
4.2 115984
 
1.7%
6.0 90035
 
1.3%
2.3 65630
 
0.9%
Other values (28) 402083
 
5.8%
2025-04-03T20:19:09.847627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4612802
19.3%
. 2463284
10.3%
d 2305948
9.7%
a 2305948
9.7%
p 2259734
9.5%
u 2259734
9.5%
n 2259734
9.5%
4 1901876
8.0%
0 890164
 
3.7%
1 697508
 
2.9%
Other values (18) 1894387
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23851119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4612802
19.3%
. 2463284
10.3%
d 2305948
9.7%
a 2305948
9.7%
p 2259734
9.5%
u 2259734
9.5%
n 2259734
9.5%
4 1901876
8.0%
0 890164
 
3.7%
1 697508
 
2.9%
Other values (18) 1894387
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23851119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4612802
19.3%
. 2463284
10.3%
d 2305948
9.7%
a 2305948
9.7%
p 2259734
9.5%
u 2259734
9.5%
n 2259734
9.5%
4 1901876
8.0%
0 890164
 
3.7%
1 697508
 
2.9%
Other values (18) 1894387
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23851119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4612802
19.3%
. 2463284
10.3%
d 2305948
9.7%
a 2305948
9.7%
p 2259734
9.5%
u 2259734
9.5%
n 2259734
9.5%
4 1901876
8.0%
0 890164
 
3.7%
1 697508
 
2.9%
Other values (18) 1894387
7.9%
Distinct758371
Distinct (%)32.8%
Missing33
Missing (%)< 0.1%
Memory size145.0 MiB
2025-04-03T20:19:10.199928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length43
Mean length14.553044
Min length1

Characters and Unicode

Total characters33,659,896
Distinct characters5,008
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique490,445 ?
Unique (%)21.2%

Sample

1st rowJean Confident Irénée NIYIZIBYOSE
2nd rowWebserveis
3rd rowCabin Crew
4th rowClimate Smart Tech2
5th rowRafal Milek-Horodyski
ValueCountFrequency (%)
apps 115392
 
2.3%
inc 87566
 
1.8%
ltd 73101
 
1.5%
games 65597
 
1.3%
64586
 
1.3%
studio 58860
 
1.2%
llc 40722
 
0.8%
app 36754
 
0.7%
media 32839
 
0.7%
solutions 31084
 
0.6%
Other values (515996) 4379288
87.8%
2025-04-03T20:19:10.636300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2677975
 
8.0%
e 2458145
 
7.3%
a 2259521
 
6.7%
o 1956385
 
5.8%
i 1934924
 
5.7%
n 1569447
 
4.7%
t 1471572
 
4.4%
r 1447802
 
4.3%
s 1368490
 
4.1%
l 1105184
 
3.3%
Other values (4998) 15410451
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33659896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2677975
 
8.0%
e 2458145
 
7.3%
a 2259521
 
6.7%
o 1956385
 
5.8%
i 1934924
 
5.7%
n 1569447
 
4.7%
t 1471572
 
4.4%
r 1447802
 
4.3%
s 1368490
 
4.1%
l 1105184
 
3.3%
Other values (4998) 15410451
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33659896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2677975
 
8.0%
e 2458145
 
7.3%
a 2259521
 
6.7%
o 1956385
 
5.8%
i 1934924
 
5.7%
n 1569447
 
4.7%
t 1471572
 
4.4%
r 1447802
 
4.3%
s 1368490
 
4.1%
l 1105184
 
3.3%
Other values (4998) 15410451
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33659896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2677975
 
8.0%
e 2458145
 
7.3%
a 2259521
 
6.7%
o 1956385
 
5.8%
i 1934924
 
5.7%
n 1569447
 
4.7%
t 1471572
 
4.4%
r 1447802
 
4.3%
s 1368490
 
4.1%
l 1105184
 
3.3%
Other values (4998) 15410451
45.8%

Developer Website
Text

Missing 

Distinct810440
Distinct (%)52.2%
Missing760835
Missing (%)32.9%
Memory size137.1 MiB
2025-04-03T20:19:11.050152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length617
Median length231
Mean length27.920862
Min length9

Characters and Unicode

Total characters43,336,221
Distinct characters643
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique678,375 ?
Unique (%)43.7%

Sample

1st rowhttps://beniyizibyose.tk/#/
2nd rowhttps://webserveis.netlify.app/
3rd rowhttp://www.climatesmarttech.com/
4th rowhttp://www.horodyski.com.pl
5th rowhttp://www.imocci.com
ValueCountFrequency (%)
http://www.subsplash.com 7537
 
0.5%
http://www.chownow.com 4508
 
0.3%
http://www.sharefaith.com/category/church-websites.html 2057
 
0.1%
https://zeta-mars.blogspot.com 1877
 
0.1%
https://foodsoul.pro 1802
 
0.1%
http://www.magzter.com 1574
 
0.1%
https://vsxapps.com/currencyx 1518
 
0.1%
https://www.mindbodyonline.com/branded-apps 1508
 
0.1%
http://www.orderyoyo.com 1486
 
0.1%
http://android.atm-plushome.com/theme/index/?=am_plushome_in 1412
 
0.1%
Other values (791511) 1526832
98.4%
2025-04-03T20:19:11.547074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 4648467
 
10.7%
/ 3830912
 
8.8%
. 2813270
 
6.5%
o 2772351
 
6.4%
p 2740293
 
6.3%
w 2474797
 
5.7%
s 2185729
 
5.0%
a 2070785
 
4.8%
h 2047086
 
4.7%
e 1936347
 
4.5%
Other values (633) 15816184
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43336221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 4648467
 
10.7%
/ 3830912
 
8.8%
. 2813270
 
6.5%
o 2772351
 
6.4%
p 2740293
 
6.3%
w 2474797
 
5.7%
s 2185729
 
5.0%
a 2070785
 
4.8%
h 2047086
 
4.7%
e 1936347
 
4.5%
Other values (633) 15816184
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43336221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 4648467
 
10.7%
/ 3830912
 
8.8%
. 2813270
 
6.5%
o 2772351
 
6.4%
p 2740293
 
6.3%
w 2474797
 
5.7%
s 2185729
 
5.0%
a 2070785
 
4.8%
h 2047086
 
4.7%
e 1936347
 
4.5%
Other values (633) 15816184
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43336221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 4648467
 
10.7%
/ 3830912
 
8.8%
. 2813270
 
6.5%
o 2772351
 
6.4%
p 2740293
 
6.3%
w 2474797
 
5.7%
s 2185729
 
5.0%
a 2070785
 
4.8%
h 2047086
 
4.7%
e 1936347
 
4.5%
Other values (633) 15816184
36.5%
Distinct950456
Distinct (%)41.1%
Missing31
Missing (%)< 0.1%
Memory size157.2 MiB
2025-04-03T20:19:11.997957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length111
Median length74
Mean length22.255268
Min length6

Characters and Unicode

Total characters51,474,498
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique712,455 ?
Unique (%)30.8%

Sample

1st rowjean21101999@gmail.com
2nd rowwebserveis@gmail.com
3rd rowvnacrewit@gmail.com
4th rowclimatesmarttech2@gmail.com
5th rowrmilekhorodyski@gmail.com
ValueCountFrequency (%)
support@classplus.co 10345
 
0.4%
appsupport@subsplash.com 7570
 
0.3%
help@trainerize.com 4887
 
0.2%
eng-android@chownow.com 4862
 
0.2%
support@mindbodyonline.com 3047
 
0.1%
deploy@phorest.com 2811
 
0.1%
appsupport@sharefaith.com 2059
 
0.1%
jymstudio2019@gmail.com 1961
 
0.1%
marszeta@gmail.com 1883
 
0.1%
android@foodsoul.pro 1803
 
0.1%
Other values (944564) 2271685
98.2%
2025-04-03T20:19:12.523406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4620134
 
9.0%
a 4563370
 
8.9%
m 4355097
 
8.5%
i 3689797
 
7.2%
c 3111979
 
6.0%
. 2826713
 
5.5%
e 2797463
 
5.4%
l 2727952
 
5.3%
@ 2312913
 
4.5%
s 2082710
 
4.0%
Other values (121) 18386370
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51474498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4620134
 
9.0%
a 4563370
 
8.9%
m 4355097
 
8.5%
i 3689797
 
7.2%
c 3111979
 
6.0%
. 2826713
 
5.5%
e 2797463
 
5.4%
l 2727952
 
5.3%
@ 2312913
 
4.5%
s 2082710
 
4.0%
Other values (121) 18386370
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51474498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4620134
 
9.0%
a 4563370
 
8.9%
m 4355097
 
8.5%
i 3689797
 
7.2%
c 3111979
 
6.0%
. 2826713
 
5.5%
e 2797463
 
5.4%
l 2727952
 
5.3%
@ 2312913
 
4.5%
s 2082710
 
4.0%
Other values (121) 18386370
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51474498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4620134
 
9.0%
a 4563370
 
8.9%
m 4355097
 
8.5%
i 3689797
 
7.2%
c 3111979
 
6.0%
. 2826713
 
5.5%
e 2797463
 
5.4%
l 2727952
 
5.3%
@ 2312913
 
4.5%
s 2082710
 
4.0%
Other values (121) 18386370
35.7%

Released
Date

Missing 

Distinct4158
Distinct (%)0.2%
Missing71053
Missing (%)3.1%
Memory size17.6 MiB
Minimum2010-01-28 00:00:00
Maximum2021-06-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-03T20:19:12.583957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:19:12.659521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3918
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 MiB
Minimum2009-02-09 00:00:00
Maximum2021-06-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-03T20:19:12.733084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:19:12.805645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Content Rating
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 MiB
Everyone
2022089 
Teen
 
196375
Mature 17+
 
60289
Everyone 10+
 
33901
Unrated
 
154

Length

Max length15
Median length8
Mean length7.7714947
Min length4

Characters and Unicode

Total characters17,975,032
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEveryone
2nd rowEveryone
3rd rowEveryone
4th rowEveryone
5th rowEveryone

Common Values

ValueCountFrequency (%)
Everyone 2022089
87.4%
Teen 196375
 
8.5%
Mature 17+ 60289
 
2.6%
Everyone 10+ 33901
 
1.5%
Unrated 154
 
< 0.1%
Adults only 18+ 136
 
< 0.1%

Length

2025-04-03T20:19:12.873203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-03T20:19:12.915239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
everyone 2055990
85.4%
teen 196375
 
8.2%
mature 60289
 
2.5%
17 60289
 
2.5%
10 33901
 
1.4%
unrated 154
 
< 0.1%
adults 136
 
< 0.1%
only 136
 
< 0.1%
18 136
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 4565173
25.4%
n 2252655
12.5%
r 2116433
11.8%
y 2056126
11.4%
o 2056126
11.4%
v 2055990
11.4%
E 2055990
11.4%
T 196375
 
1.1%
94462
 
0.5%
1 94326
 
0.5%
Other values (13) 431376
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17975032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4565173
25.4%
n 2252655
12.5%
r 2116433
11.8%
y 2056126
11.4%
o 2056126
11.4%
v 2055990
11.4%
E 2055990
11.4%
T 196375
 
1.1%
94462
 
0.5%
1 94326
 
0.5%
Other values (13) 431376
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17975032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4565173
25.4%
n 2252655
12.5%
r 2116433
11.8%
y 2056126
11.4%
o 2056126
11.4%
v 2055990
11.4%
E 2055990
11.4%
T 196375
 
1.1%
94462
 
0.5%
1 94326
 
0.5%
Other values (13) 431376
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17975032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4565173
25.4%
n 2252655
12.5%
r 2116433
11.8%
y 2056126
11.4%
o 2056126
11.4%
v 2055990
11.4%
E 2055990
11.4%
T 196375
 
1.1%
94462
 
0.5%
1 94326
 
0.5%
Other values (13) 431376
 
2.4%

Privacy Policy
Text

Missing 

Distinct977743
Distinct (%)51.7%
Missing420953
Missing (%)18.2%
Memory size194.2 MiB
2025-04-03T20:19:13.420669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9,957
Median length415
Mean length51.345085
Min length10

Characters and Unicode

Total characters97,144,438
Distinct characters1,014
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique847,205 ?
Unique (%)44.8%

Sample

1st rowhttps://beniyizibyose.tk/projects/
2nd rowhttps://dev4phones.wordpress.com/licencia-de-uso/
3rd rowhttps://www.vietnamairlines.com/vn/en/terms-and-conditions/privacy-policy
4th rowhttp://www.horodyski.com.pl
5th rowhttps://www.imocci.com/wp-content/uploads/2018/08/Datenschutzerklärung_IMOCCI_22072018.pdf
ValueCountFrequency (%)
http://www.subsplash.com/legal/privacy 7481
 
0.4%
https://unity3d.com/legal/privacy-policy 5315
 
0.3%
http://www.trainerize.com/privacy.aspx 5181
 
0.3%
http://www.chownow.com/privacy-policy 4784
 
0.3%
https://classplusapp.com/privacy.html 4375
 
0.2%
https://bit.ly/2ydtip0 3435
 
0.2%
https://www.mindbodyonline.com/privacy-policy 3357
 
0.2%
https://pushpay.com/legal-center/privacy 3023
 
0.2%
https://zanadoo.me/pages/privacy 2788
 
0.1%
https://quickappninja.com/game-privacy-policy.html 2419
 
0.1%
Other values (970588) 1849833
97.8%
2025-04-03T20:19:13.996159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 7480214
 
7.7%
t 7128869
 
7.3%
p 6380260
 
6.6%
o 5904013
 
6.1%
i 5397879
 
5.6%
c 5136581
 
5.3%
a 4804652
 
4.9%
e 4310747
 
4.4%
s 4204901
 
4.3%
. 4131057
 
4.3%
Other values (1004) 42265265
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97144438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 7480214
 
7.7%
t 7128869
 
7.3%
p 6380260
 
6.6%
o 5904013
 
6.1%
i 5397879
 
5.6%
c 5136581
 
5.3%
a 4804652
 
4.9%
e 4310747
 
4.4%
s 4204901
 
4.3%
. 4131057
 
4.3%
Other values (1004) 42265265
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97144438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 7480214
 
7.7%
t 7128869
 
7.3%
p 6380260
 
6.6%
o 5904013
 
6.1%
i 5397879
 
5.6%
c 5136581
 
5.3%
a 4804652
 
4.9%
e 4310747
 
4.4%
s 4204901
 
4.3%
. 4131057
 
4.3%
Other values (1004) 42265265
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97144438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 7480214
 
7.7%
t 7128869
 
7.3%
p 6380260
 
6.6%
o 5904013
 
6.1%
i 5397879
 
5.6%
c 5136581
 
5.3%
a 4804652
 
4.9%
e 4310747
 
4.4%
s 4204901
 
4.3%
. 4131057
 
4.3%
Other values (1004) 42265265
43.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
False
1162170 
True
1150774 
ValueCountFrequency (%)
False 1162170
50.2%
True 1150774
49.8%
2025-04-03T20:19:14.031189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

In App Purchases
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
False
2117635 
True
 
195309
ValueCountFrequency (%)
False 2117635
91.6%
True 195309
 
8.4%
2025-04-03T20:19:14.056209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Editors Choice
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
False
2312091 
True
 
853
ValueCountFrequency (%)
False 2312091
> 99.9%
True 853
 
< 0.1%
2025-04-03T20:19:14.079730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct67374
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size17.6 MiB
Minimum2021-06-15 20:19:35
Maximum2021-06-16 15:10:42
Invalid dates0
Invalid dates (%)0.0%
2025-04-03T20:19:14.125268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:19:14.199332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-04-03T20:18:51.505996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:46.684894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:47.878409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:49.062416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:50.347511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:51.717176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:46.913589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:48.088088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:49.370178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:50.569700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:51.934361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:47.133775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:48.399352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:49.585361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:50.796392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:52.141037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:47.448543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:48.627546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:49.811053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:50.995562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:52.433286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:47.667730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:48.843230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:50.024735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T20:18:51.225758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-03T20:19:14.257380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Ad SupportedCategoryContent RatingCurrencyEditors ChoiceFreeIn App PurchasesInstallsMaximum InstallsMinimum InstallsPriceRatingRating Count
Ad Supported1.0000.4920.1280.0030.0060.1240.1380.2550.0020.0000.0150.1960.002
Category0.4921.0000.2340.0030.0380.1060.2800.0670.0030.0040.0150.0940.005
Content Rating0.1280.2341.0000.0000.0210.0280.0960.0410.0010.0000.0010.0260.004
Currency0.0030.0030.0001.0000.0000.0020.0020.0030.0000.0000.0000.0030.000
Editors Choice0.0060.0380.0210.0001.0000.0030.0460.1880.0420.0270.0000.0270.083
Free0.1240.1060.0280.0020.0031.0000.0090.0700.0000.0000.1190.0160.000
In App Purchases0.1380.2800.0960.0020.0460.0091.0000.2830.0030.0010.0000.1790.008
Installs0.2550.0670.0410.0030.1880.0700.2831.0000.5561.0000.0130.2680.273
Maximum Installs0.0020.0030.0010.0000.0420.0000.0030.5561.0000.991-0.0480.5940.845
Minimum Installs0.0000.0040.0000.0000.0270.0000.0011.0000.9911.000-0.0480.5880.839
Price0.0150.0150.0010.0000.0000.1190.0000.013-0.048-0.0481.0000.0110.013
Rating0.1960.0940.0260.0030.0270.0160.1790.2680.5940.5880.0111.0000.804
Rating Count0.0020.0050.0040.0000.0830.0000.0080.2730.8450.8390.0130.8041.000

Missing values

2025-04-03T20:18:53.329549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-03T20:18:55.622500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-03T20:19:00.773286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

App NameApp IdCategoryRatingRating CountInstallsMinimum InstallsMaximum InstallsFreePriceCurrencySizeMinimum AndroidDeveloper IdDeveloper WebsiteDeveloper EmailReleasedLast UpdatedContent RatingPrivacy PolicyAd SupportedIn App PurchasesEditors ChoiceScraped Time
0Gakondocom.ishakwe.gakondoAdventure0.00.010+10.015True0.0USD10M7.1 and upJean Confident Irénée NIYIZIBYOSEhttps://beniyizibyose.tk/#/jean21101999@gmail.comFeb 26, 2020Feb 26, 2020Everyonehttps://beniyizibyose.tk/projects/FalseFalseFalse2021-06-15 20:19:35
1Ampere Battery Infocom.webserveis.batteryinfoTools4.464.05,000+5000.07662True0.0USD2.9M5.0 and upWebserveishttps://webserveis.netlify.app/webserveis@gmail.comMay 21, 2020May 06, 2021Everyonehttps://dev4phones.wordpress.com/licencia-de-uso/TrueFalseFalse2021-06-15 20:19:35
2Vibookcom.doantiepvien.crmProductivity0.00.050+50.058True0.0USD3.7M4.0.3 and upCabin CrewNaNvnacrewit@gmail.comAug 9, 2019Aug 19, 2019Everyonehttps://www.vietnamairlines.com/vn/en/terms-and-conditions/privacy-policyFalseFalseFalse2021-06-15 20:19:35
3Smart City Trichy Public Service Vehicles 17UCS548cst.stJoseph.ug17ucs548Communication5.05.010+10.019True0.0USD1.8M4.0.3 and upClimate Smart Tech2http://www.climatesmarttech.com/climatesmarttech2@gmail.comSep 10, 2018Oct 13, 2018EveryoneNaNTrueFalseFalse2021-06-15 20:19:35
4GROW.mecom.horodyski.growerTools0.00.0100+100.0478True0.0USD6.2M4.1 and upRafal Milek-Horodyskihttp://www.horodyski.com.plrmilekhorodyski@gmail.comFeb 21, 2020Nov 12, 2018Everyonehttp://www.horodyski.com.plFalseFalseFalse2021-06-15 20:19:35
5IMOCCIcom.imocciSocial0.00.050+50.089True0.0USD46M6.0 and upImocci GmbHhttp://www.imocci.cominfo@imocci.comDec 24, 2018Dec 20, 2019Teenhttps://www.imocci.com/wp-content/uploads/2018/08/Datenschutzerklärung_IMOCCI_22072018.pdfFalseTrueFalse2021-06-15 20:19:35
6unlimited 4G data prank free appgetfreedata.superfatiza.unlimitedjiodataprankLibraries & Demo4.512.01,000+1000.02567True0.0USD2.5M4.1 and upandroid developer779NaNaitomgharfatimezzahra@gmail.comSep 23, 2019Sep 27, 2019Everyonehttps://sites.google.com/view/unlimited4gdataprankTrueFalseFalse2021-06-15 20:19:35
7The Everyday Calendarcom.mozaix.simoneboardLifestyle2.039.0500+500.0702True0.0USD16M5.0 and upMozaix LLCNaNelementuser03@gmail.comJun 21, 2019Jun 21, 2019Everyonehttps://www.freeprivacypolicy.com/privacy/view/978b22a2fd432de423de81e4ac91d571FalseFalseFalse2021-06-15 20:19:35
8WhatsOpencom.whatsopen.appCommunication0.00.010+10.018True0.0USD1.3M4.4 and upYilver Molina Hurtatizhttp://yilvermolinah.comyilver.mh1996@gmail.comNaNDec 07, 2018Teenhttp://elcafedelamanana.yilvermolinah.com/policy.htmlFalseFalseFalse2021-06-15 20:19:35
9Neon 3d Iron Tech Keyboard Themecom.ikeyboard.theme.neon_3d.iron.techPersonalization4.7820.050,000+50000.062433True0.0USD3.5M4.1 and upFree 2021 Themes for Emoji keyboardhttps://trendyteme888-31139.web.apptrendyteme.888@gmail.comSep 22, 2019Oct 07, 2020Everyonehttp://bit.ly/EmojiThemeProTrueFalseFalse2021-06-15 20:19:35
App NameApp IdCategoryRatingRating CountInstallsMinimum InstallsMaximum InstallsFreePriceCurrencySizeMinimum AndroidDeveloper IdDeveloper WebsiteDeveloper EmailReleasedLast UpdatedContent RatingPrivacy PolicyAd SupportedIn App PurchasesEditors ChoiceScraped Time
2312934Vietnamese - English Translatorcom.eliminatesapps.vietnamesetranslatorEducation0.00.05+5.06True0.0USD3.6M4.0 and upEliminates Appshttps://af24d8239.app-ads-txt.comeliminatesapps@gmail.comJun 15, 2020Aug 31, 2020Everyonehttps://sites.google.com/view/redli/homeTrueFalseFalse2021-06-16 12:59:18
2312935Floral Wallpapercom.arfdev.floralwallpaperPersonalization0.00.01,000+1000.01302True0.0USD29M4.1 and uparfdevNaNarfdev56@gmail.comJul 19, 2018Nov 13, 2019Everyonehttps://docs.google.com/document/d/14UGI886tB6hfBN7hLpAq0wZPwBK1dzIH1o2Xm1mg6FA/edit?usp=sharingTrueFalseFalse2021-06-16 12:59:18
2312936Engineers Careerscom.eventapps.eventappsBusiness0.00.0100+100.0353True0.0USD21M5.0 and upEventiNaNrami.osama.selim@gmail.comMar 5, 2020Feb 29, 2020EveryoneNaNFalseFalseFalse2021-06-16 12:59:18
2312937STMIK Mercusuar - Aditya Rachmancom.aplikasi.datapribadiaditEducation0.00.05+5.07True0.0USD6.6M4.4 and upSTMIK MercusuarNaNrachmanaditya5@gmail.comJan 15, 2020Jan 15, 2020EveryoneNaNFalseFalseFalse2021-06-16 12:59:18
2312938Lero TOEFL Recorder + Timercom.toefltimerEducation3.417.01,000+1000.01980True0.0USD10M4.1 and upJulio Augusto Verahttps://lerodoe.wordpress.com/lero.doe@gmail.comMay 22, 2018Dec 14, 2018Everyonehttps://lerodoe.wordpress.com/app-privacy-policy-timer-recorder/TrueFalseFalse2021-06-16 12:59:18
2312939大俠客—熱血歸來com.rxsj.ssjjRole Playing4.316775.0100,000+100000.0337109True0.0USD77M4.1 and upALICE GAMEhttp://www.4399sy.com.hk/ssjjcomhk@gmail.comNaNJun 01, 2021Teenhttp://a.4399sy.com.hk/user/aggreementFalseFalseFalse2021-06-16 12:59:18
2312940ORU Onlinecom.threedream.oruonlineEducation0.00.0100+100.0430True0.0USD44M4.1 and up3Dream Studios, LLChttp://www.oru.edu/3DreamDeveloper@gmail.comJan 17, 2018Feb 02, 2018Everyonehttp://www.oru.edu/about-oru/privacy-policy.phpFalseFalseFalse2021-06-16 12:59:19
2312941Data Structuredatastructure.appoworld.datastuctureEducation0.00.0100+100.0202True0.0USD29M5.0 and upappoworldNaNappoworld.official@gmail.comAug 19, 2018Aug 19, 2018Everyonehttps://appoworld.000webhostapp.com/datastructure.htmlFalseFalseFalse2021-06-16 12:59:19
2312942Devi Suktamishan.devi.suktamMusic & Audio3.58.01,000+1000.02635True0.0USD10M5.0 and upBhaktihttps://a70f78905.app-ads-txt.comruchisono@gmail.comAug 1, 2016May 05, 2021Everyonehttps://docs.google.com/document/d/1x-9reZuLRXNL_GFGpj8wMD4A0f49f7YPiUkAMJwNyNQ/edit?usp=sharingTrueFalseFalse2021-06-16 12:59:19
2312943Biliyor Musun - Sonsuz Yarışcom.yyazilim.biliyormusunTrivia5.012.0100+100.0354True0.0USD5.2M5.0 and upY YazılımNaNyyazilimdevelop@gmail.comAug 9, 2019Aug 19, 2019Everyonehttps://biliyor-musun-sons.flycricket.io/privacy.htmlTrueFalseFalse2021-06-16 12:59:19